The use of synthetic transmit aperture ultrasound imaging (STAU) for tissue imaging has been considered for some time [1, 2, 3, 4, 5, 6]. It has not been in clinical use because of the relatively higher complexity of the hardware and because of its inability to estimate blood velocities. One of the requirements for estimating the flow is to have RF data acquired in the same direction at high pulse repetition frequencies. The synthetic aperture algorithms acquire the data for a single RF line over a number of emissions. The fastest approach to acquire data is the synthetic transmit aperture acquisition [7, 8, 9, 10]. Even so, at least two emissions are necessary to beam form the data, and the beam formation is then on data that are displaced relative to each other.
A beam forming method allowing for a new image to be created at every emission has previously been suggested [11]. This method overcomes the first limitation of the synthetic aperture imaging, as there is a scan line at every emission for every direction. This allows for a whole flow map to be created at every emission. The data, however, still suffers from motion artifacts.
Previous attempts have been confined to the estimation of gross tissue motion for motion compensation [12, 13]. The approach suggested by Nock and Trahey [12] uses cross-correlation between the received raw RF signals. The algorithm, however, relies on the fact the transmission is focused, and that the received signals come from the same direction. It is therefore not suitable for STAU. The method suggested by Bilge et al. [13] relies on the cross-correlation between low-resolution images, which are formed using the same transmit-receive pairs of transducer elements. The beam is, however, broad and the blood cells within its limits have a wide spread of velocities. This results in an increased bias and uncertainty of the estimates.
Both types of motion compensation schemes show higher performance when the images were obtained using the same transmit/receive element pairs, or in other words have the same spatial frequencies. The high-resolution images (HRI) have the highest overlap of spatial frequencies, and therefore they should give the best estimates. The correlation of the signals received from the blood cells decreases rapidly due to migration of scatterers, beam-width modulation, and flow gradients (change in the relative positions between the scatterers) [14]. The HRIs must be generated after every emission, which is possible using recursive ultrasound imaging. These images suffer from motion artifacts, which changes from frame to frame. In [15] it was shown that it is possible to both compensate for motion artifacts and estimate the velocity from the motion-compensated HRIs. The success of the velocity estimation relies on the success of the motion compensation, which makes the whole approach unstable. The purpose of the method suggested is to avoid motion compensation prior to velocity estimation.